Multi-View Hierarchical Representation Learning of Fetal Hemodynamics for Maternal Hypertension Detection at the Edge

TL;DR

This paper introduces AutoHyPE, a hierarchical attention network that analyzes fetal Doppler ultrasound signals to detect maternal hypertension, enabling continuous, non-invasive prenatal monitoring with high accuracy.

Contribution

The study presents a novel deep learning model that captures fetal cardiovascular signals for maternal hypertension detection, outperforming baseline methods and suitable for edge deployment.

Findings

AutoHyPE achieved an AUROC of 0.80 for hypertension detection.

The model maintains balanced performance across classes and in edge scenarios.

Fetal cardiac activity contains markers indicative of maternal hypertension.

Abstract

Hypertensive disorders of pregnancy remain a leading cause of maternal and fetal morbidity worldwide, yet diagnosis relies on intermittent cuff-based blood pressure measurements that are prone to bias and fail to capture continuous physiological dynamics. Growing evidence suggests that fetal cardiovascular activity is associated with maternal-placental hemodynamics and may encode markers of maternal hypertension. To analyze this, we collected a large-scale dataset of fetal one-dimensional Doppler ultrasound recordings paired with maternal blood pressure from 3,255 pregnant women across 8,170 antenatal visits in rural Guatemala. We developed AutoHyPE, a hierarchical attention network that models short- and long-term signal structure, incorporating a novel prototype-based contrastive learning and multi-view strategy to enhance representation robustness under long-tailed class distribution and biological variability. AutoHyPE achieved an AUROC of 0.80 for maternal hypertension detection, outperforming baseline approaches while maintaining balanced performance across classes, with no performance degradation in an edge deployment scenario. Our findings demonstrated that fetal cardiac mechanical activity contains hemodynamic features indicative of maternal hypertension status. This supports a promising paradigm shift toward continuous, objective monitoring of maternal health using existing, low-cost ultrasound technology and introduces a complementary approach to traditional methods based on blood pressure measurements, advancing scalable prenatal care.